(Hyper)-Graphical Models in Biomedical Image Analysis
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Computational vision, visual computing and biomedical image analysis have made tremendous progress over the past two decades. This is mostly due the development of efficient learning and inference algorithms which allow better and richer modeling of image and visual understanding tasks. Hyper-graph representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization problem. In this paper, we briefly introduce the importance of such representations, discuss their strength and limitations, provide appropriate strategies for their inference and present their application to address a variety of problems in biomedical image analysis.
Date Issued
2016-06-23
Date Acceptance
2016-06-13
Citation
Medical Image Analysis, 2016, 33, pp.102-106
ISSN
1361-8423
Publisher
Elsevier
Start Page
102
End Page
106
Journal / Book Title
Medical Image Analysis
Volume
33
Copyright Statement
© 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Subjects
(Hyper)graphs
Graph cuts
Image segmentation
Linear programming
Message passing
Random fields
Shape & volume registration
Nuclear Medicine & Medical Imaging
09 Engineering
11 Medical And Health Sciences
Publication Status
Published